School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2023 Jan 15;23(2):1002. doi: 10.3390/s23021002.
Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the perspective of hybridizing two existing linear clustering frameworks. Instead of conventional grid sampling seeds for region clustering, a fast convergence strategy is first introduced to center the final superpixel clusters, which is based on an accelerated convergence strategy. Superpixels are then generated from a center-fixed online average clustering, which adopts region growing to label all pixels in an efficient one-pass manner. The experiments verify that the integration of this two-step implementation could generate a synergistic effect and that it becomes more well-rounded than each single method. Compared with other state-of-the-art superpixel algorithms, the proposed framework achieves a comparable overall performance in terms of segmentation accuracy, spatial compactness and running efficiency; moreover, an application on image segmentation verifies its facilitation for traffic scene analysis.
超像素分解可以通过有意义的片段重建图像，以提取区域特征，从而提高先进计算机视觉任务的性能。为了进一步优化计算效率和分割质量，提出了一种新的框架，从混合两种现有线性聚类框架的角度生成超像素。新框架不是使用传统的网格采样种子进行区域聚类，而是首先引入一种快速收敛策略来集中最终的超像素聚类，该策略基于加速收敛策略。然后，从中心固定的在线平均聚类生成超像素，采用区域生长以高效的单遍方式标记所有像素。实验验证了这种两步实现的集成可以产生协同效应，并且比每个单独的方法更加全面。与其他最先进的超像素算法相比，所提出的框架在分割准确性、空间紧凑性和运行效率方面具有相当的整体性能；此外，在图像分割上的应用验证了其对交通场景分析的促进作用。
